DI MIAO: CLASS-SENSITIVE PRINCIPAL COMPONENTS ANALYSIS (Under the direction of J. S. Marron and Jason P. Fine) Research in a number of fields requires the analysis of complex datasets. Principal Components Analysis (PCA) is a popular exploratory method. However it is driven entirely by variation in the dataset without using any predefined class label information. Linear classifiers make up a fa...

In this paper, we propose a WCET analysis that focuses on two aspects. First, it supports contextsensitive hardware and software timing effects, meaning that it is sensitive to the execution history of the program and thus can account for effects like cache persistence, triangular loop, etc. Second, it supports the introduction of parameters in both the software model (e.g. parametric loop boun...

The way we represent intentions, behaviors, and the spatial context, is crucial for any approach to mobile intention recognition. Formal grammars are cognitively comprehensible and make expressiveness properties explicit. By adding spatial domain knowledge to a grammar we can reduce parsing ambiguities. We argue that there are a number of mobile intention recognition problems which require the ...

To prevent an exploit, the security analyst must implement a suitable countermeasure. In this paper, we consider cost-sensitive attack graphs (CAGs) for network vulnerability analysis. In these attack graphs, a weight is assigned to each countermeasure to represent the cost of its implementation. There may be multiple countermeasures with different weights for preventing a single exploit. Also,...